Eprime is a set of programs for administering psychological experiments by computer. This package provides functions for loading, parsing, filtering and exporting data in the text files produced by Eprime experiments.

This package contains various tools to perform and visualize Response Item Networks ('ResIN's'). ResIN dummy-codes ordered and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.

Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of ruminate is to create a shiny interface to make these tools more broadly available while creating reproducible results.

An R Interface to EPP-lab v1.0. EPP-lab is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.

This package provides a toolkit for Commodities analytics', risk management and trading professionals. Includes functions for API calls to <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.

This package provides a rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.

This package provides a Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.

This package provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention). Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be high risk by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.

Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple redistverse packages at once. The redistverse is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.

Vector Graphics devices for Microsoft PowerPoint and Microsoft Excel'. Functions extending package officer are provided to embed DrawingML graphics into Microsoft PowerPoint presentations and Microsoft Excel workbooks.

We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.

Implementation of the metalog distribution in R. The metalog distribution is a modern, highly flexible, data-driven distribution. Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>. This package provides functions to build these distributions from raw data. Resulting metalog objects are then useful for exploratory and probabilistic analysis.

This package implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.

Converting ascii text into (floating-point) numeric values is a very common problem. The fast_float header-only C++ library by Daniel Lemire does it very well and very fast at up to or over to 1 gigabyte per second as described in more detail in <doi:10.1002/spe.2984>. fast_float is licensed under the Apache 2.0 license and provided here for use by other R packages via a simple LinkingTo: statement.

Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

This package provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.

Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the Hit Rate is calculated), RR@K (reciprocal rank at k, from which the MRR or mean reciprocal rank is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.

The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

This package contains a function to randomize subjects, patients in groups of sequences (treatment sequences). If a blocksize is given, the randomization will be done within blocks. The randomization may be controlled by a Wald-Wolfowitz runs test. Functions to obtain the p-value of that test are included. The package is mainly intended for randomization of bioequivalence studies but may be used also for other clinical crossover studies. Contains two helper functions sequences() and williams() to get the sequences of commonly used designs in BE studies.

This package provides data structures and functions for file input/output in the ribios software suite, supporting common bioinformatics and computational biology file formats, designed for fast loading and high performance with minimal dependencies.

Facilitates the use of machine learning algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.5.0 improved mparheuristic function (new hyperparameter heuristics); 1.4.9 / 1.4.8 improved help, several warning and error code fixes (more stable version, all examples run correctly); 1.4.7 - improved Importance function and examples, minor error fixes; 1.4.6 / 1.4.5 / 1.4.4 new automated machine learning (AutoML) and ensembles, via improved fit(), mining() and mparheuristic() functions, and new categorical preprocessing, via improved delevels() function; 1.4.3 new metrics (e.g., macro precision, explained variance), new "lssvm" model and improved mparheuristic() function; 1.4.2 new "NMAE" metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics; 1.2 - new input importance methods via improved Importance() function; 1.0 - first version.

This package provides a user-friendly interface to NASA Exoplanets Archive API <https://exoplanetarchive.ipac.caltech.edu/>, enabling retrieval and analysis of exoplanetary and stellar data. Includes functions for querying, filtering, summarizing, and computing derived parameters from the Exoplanets catalog.

Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.